SOTAVerified

Self-Supervised Learning

Self-Supervised Learning is proposed for utilizing unlabeled data with the success of supervised learning. Producing a dataset with good labels is expensive, while unlabeled data is being generated all the time. The motivation of Self-Supervised Learning is to make use of the large amount of unlabeled data. The main idea of Self-Supervised Learning is to generate the labels from unlabeled data, according to the structure or characteristics of the data itself, and then train on this unsupervised data in a supervised manner. Self-Supervised Learning is wildly used in representation learning to make a model learn the latent features of the data. This technique is often employed in computer vision, video processing and robot control.

Source: Self-supervised Point Set Local Descriptors for Point Cloud Registration

Image source: LeCun

Papers

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Showing 4551–4600 of 5044 papers

TitleStatusHype
RAZE: Region Guided Self-Supervised Gaze Representation Learning—0
RCA: Ride Comfort-Aware Visual Navigation via Self-Supervised Learning—0
REaaS: Enabling Adversarially Robust Downstream Classifiers via Robust Encoder as a Service—0
RealMonoDepth: Self-Supervised Monocular Depth Estimation for General Scenes—0
Real-Time Cattle Interaction Recognition via Triple-stream Network—0
Real-time Virtual-Try-On from a Single Example Image through Deep Inverse Graphics and Learned Differentiable Renderers—0
Reason from Context with Self-supervised Learning—0
Reasoning-Modulated Representations—0
Reblur2Deblur: Deblurring Videos via Self-Supervised Learning—0
Recent Advances of Local Mechanisms in Computer Vision: A Survey and Outlook of Recent Work—0
Reconstruction Enhanced Multi-View Contrastive Learning for Anomaly Detection on Attributed Networks—0
Recurrent Joint Embedding Predictive Architecture with Recurrent Forward Propagation Learning—0
Reduced order modeling for flow and transport problems with Barlow Twins self-supervised learning—0
Reducing Barriers to Self-Supervised Learning: HuBERT Pre-training with Academic Compute—0
Reducing self-supervised learning complexity improves weakly-supervised classification performance in computational pathology—0
Reducing Source-Private Bias in Extreme Universal Domain Adaptation—0
Refactoring Policy for Compositional Generalizability using Self-Supervised Object Proposals—0
Referring Self-supervised Learning on 3D Point Cloud—0
Refining Self-Supervised Learning in Imaging: Beyond Linear Metric—0
RegCLR: A Self-Supervised Framework for Tabular Representation Learning in the Wild—0
Regeneration Learning: A Learning Paradigm for Data Generation—0
Regional Deep Atrophy: a Self-Supervised Learning Method to Automatically Identify Regions Associated With Alzheimer's Disease Progression From Longitudinal MRI—0
Relating Events and Frames Based on Self-Supervised Learning and Uncorrelated Conditioning for Unsupervised Domain Adaptation—0
Relation Modeling and Distillation for Learning with Noisy Labels—0
Relative Position Prediction as Pre-training for Text Encoders—0
Remixing-based Unsupervised Source Separation from Scratch—0
Remote Heart Rate Monitoring in Smart Environments from Videos with Self-supervised Pre-training—0
Remote Sensing Image Scene Classification with Self-Supervised Paradigm under Limited Labeled Samples—0
Global and Local Contrastive Self-Supervised Learning for Semantic Segmentation of HR Remote Sensing Images—0
Remote Sensing Scene Classification with Masked Image Modeling (MIM)—0
Repeat after me: Self-supervised learning of acoustic-to-articulatory mapping by vocal imitation—0
Repoformer: Selective Retrieval for Repository-Level Code Completion—0
Representation Learning by Ranking under multiple tasks—0
Representation Learning for Wearable-Based Applications in the Case of Missing Data—0
Representation Learning of Geometric Trees—0
Representation Learning with Video Deep InfoMax—0
Representation Synthesis by Probabilistic Many-Valued Logic Operation in Self-Supervised Learning—0
Representative & Fair Synthetic Data—0
Reprogramming Self-supervised Learning-based Speech Representations for Speaker Anonymization—0
Residual Contrastive Learning for Image Reconstruction: Learning Transferable Representations from Noisy Images—0
Residual Contrastive Learning: Unsupervised Representation Learning from Residuals—0
Residual Energy-Based Models for End-to-End Speech Recognition—0
Residual-Guided Learning Representation for Self-Supervised Monocular Depth Estimation—0
Residual Vision Transformer (ResViT) Based Self-Supervised Learning Model for Brain Tumor Classification—0
Re-Simulation-based Self-Supervised Learning for Pre-Training Foundation Models—0
Resource and data efficient self supervised learning—0
Respiratory Inhaler Sound Event Classification Using Self-Supervised Learning—0
Response Selection for Multi-Party Conversations withDynamic Topic Tracking—0
Response Selection for Multi-Party Conversations with Dynamic Topic Tracking—0
RestainNet: a self-supervised digital re-stainer for stain normalization—0
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All datasetsDABSSTL-10CIFAR10cifar100ImageNet-100 (TEMI Split)TinyImageNetCIFAR-10CIFAR-100CREMA-DTiny ImageNet

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Pretraining: NoneImages & Text57.5—Unverified
2Pretraining: ShEDImages & Text54.3—Unverified
3Pretraining: e-MixImages & Text48.9—Unverified
#ModelMetricClaimedVerifiedStatus
1ResNet50Accuracy91.7—Unverified
2ResNet18Accuracy91.02—Unverified
3MV-MRAccuracy89.67—Unverified
#ModelMetricClaimedVerifiedStatus
1ResNet50average top-1 classification accuracy93.89—Unverified
2ResNet18average top-1 classification accuracy92.58—Unverified
#ModelMetricClaimedVerifiedStatus
1ResNet50average top-1 classification accuracy72.51—Unverified
2ResNet18average top-1 classification accuracy69.31—Unverified
#ModelMetricClaimedVerifiedStatus
1CorInfomax (ResNet50)Top-1 Accuracy82.64—Unverified
2CorInfomax (ResNet18)Top-1 Accuracy80.48—Unverified
#ModelMetricClaimedVerifiedStatus
1ResNet50average top-1 classification accuracy51.84—Unverified
2ResNet18average top-1 classification accuracy51.67—Unverified
#ModelMetricClaimedVerifiedStatus
1CorInfomax (ResNet18)Top-1 Accuracy93.18—Unverified
#ModelMetricClaimedVerifiedStatus
1CorInfomax (ResNet18)Top-1 Accuracy71.61—Unverified
#ModelMetricClaimedVerifiedStatus
1Hybrid BYOL-S/CvTAccuracy67.2—Unverified
#ModelMetricClaimedVerifiedStatus
1CorInfomax (ResNet50)Top-1 Accuracy54.86—Unverified